A method for estimating a capacity of a battery unit in an energy storage system of a vehicle is provided. The method includes during at least a first no-load condition of the battery unit, measuring a terminal voltage of the battery unit at a number of points in time to determine a transient voltage response of the battery unit, from the transient voltage response of the battery unit during at least the first no-load condition, estimating at least a first value of an open circuit voltage of the battery unit by means of a battery model, and estimating the capacity of the battery unit based on at least the estimated at least first value of the open circuit voltage.

A method for estimating a capacity of a battery unit in an energy storage system of a vehicle is provided. The method includes during at least a first no-load condition of the battery unit, measuring a terminal voltage of the battery unit at a number of points in time to determine a transient voltage response of the battery unit, from the transient voltage response of the battery unit during at least the first no-load condition, estimating at least a first value of an open circuit voltage of the battery unit by means of a battery model, and estimating the capacity of the battery unit based on at least the estimated at least first value of the open circuit voltage.

A battery internal temperature information processing method, a computer device, and a storage medium that first acquire off-line testing data for off-line testing a battery module and construct an equivalent thermal network model from the off-line testing data, determine optimal model parameters of the equivalent thermal network model based on a multi-objective function fitting method; thereafter, a first battery internal temperature estimate of the battery of the vehicle at a first moment in actual operation of the vehicle is determined, in turn, based on the acquired initial state vector values of the battery of the vehicle, first operational data at a first moment in actual operation of the vehicle, and an equivalent thermal network model including the optimal model parameters.

A battery internal temperature information processing method, a computer device, and a storage medium that first acquire off-line testing data for off-line testing a battery module and construct an equivalent thermal network model from the off-line testing data, determine optimal model parameters of the equivalent thermal network model based on a multi-objective function fitting method; thereafter, a first battery internal temperature estimate of the battery of the vehicle at a first moment in actual operation of the vehicle is determined, in turn, based on the acquired initial state vector values of the battery of the vehicle, first operational data at a first moment in actual operation of the vehicle, and an equivalent thermal network model including the optimal model parameters.

A simulation system of the present disclosure for performing an experiment on a battery pack including a plurality of battery cells by using at least one experimental device includes: an IoT device that receives input and output of at least one experimental device, and pre-processes the input and the output to generate input data and output data; an edge computing device for predicting information for predicting a capacity degradation value of a plurality of battery cells by using an equivalent circuit model module and a thermal model module based on data received among the input data and the output data; and a server that receives the predicted information from the edge computing device, predicts a capacity degradation value of a plurality of battery cells by using an aging model module, and predicts a defect rate based on the predicted capacity degradation values. The data size of the input data and output data transmitted from the IoT device to the edge computing device may be smaller than a predetermined data size.

Various embodiments include a method for ascertaining a mean capacity loss of a battery storage device. The method may include: providing results of a measurement series on the battery storage device taken during a plurality of load cycles, each including a charging and a discharging phase; ascertaining a first and a second discharge capacity of the device using a first and a second calculation rule, wherein each calibration is different for correcting values of the current measurement; and carrying out an optimization process so a calibration of the current measurement is ascertained to achieve the greatest match of the ascertained first discharge capacity and the second discharge capacity.

Various embodiments include a method for ascertaining a mean capacity loss of a battery storage device. The method may include: providing results of a measurement series on the battery storage device taken during a plurality of load cycles, each including a charging and a discharging phase; ascertaining a first and a second discharge capacity of the device using a first and a second calculation rule, wherein each calibration is different for correcting values of the current measurement; and carrying out an optimization process so a calibration of the current measurement is ascertained to achieve the greatest match of the ascertained first discharge capacity and the second discharge capacity.

The present disclosure relates to a method for optimizing the formation protocol of a battery. The method can include the steps of: (a) providing a battery cell structure comprising an anode, an electrolyte, and a cathode including cations that move from the cathode to the anode during charging; (b) performing a first charge of the battery cell structure using a predetermined formation protocol to create a formed battery cell; and (c) determining a cell internal resistance of the formed battery cell. Therefore, one can compare the cell internal resistances of two battery cells formed by using identical battery cell structures and different formation protocols, and select a formation protocol if the first cell internal resistance of a first formed battery is greater than or less than the second cell internal resistance of a second formed battery.

The present disclosure relates to a method for optimizing the formation protocol of a battery. The method can include the steps of: (a) providing a battery cell structure comprising an anode, an electrolyte, and a cathode including cations that move from the cathode to the anode during charging; (b) performing a first charge of the battery cell structure using a predetermined formation protocol to create a formed battery cell; and (c) determining a cell internal resistance of the formed battery cell. Therefore, one can compare the cell internal resistances of two battery cells formed by using identical battery cell structures and different formation protocols, and select a formation protocol if the first cell internal resistance of a first formed battery is greater than or less than the second cell internal resistance of a second formed battery.

A battery monitoring system continuously calculates the estimated runtime of a bank of batteries in a battery backup system during both a period of operation when the load current is supplied by a commercial source of AC power and during a period of operation when the commercial source of AC power is not present and the load current is supplied by the bank. The estimated runtime may be displayed to an operator and used to alert the operator if the cutoff voltage of a battery in the bank is at or near its cutoff voltage. The system may open a circuit breaker to avoid catastrophic damage before the cutoff voltage is reached.